PointConv [1]} and KPConv [2]} follow continuous convolution strategy without quantization. Basically, these methods directly apply convolution to the 3D point set. It is a density-weighted convolution that can fully estimate the 3D convolution on any unordered set of 3D points. The weight and density functions are estimated by MLP networks and kernel, respectively. The work in [3]} cannot capture the local structure, therefore making it difficult to learn more fine-grained patterns and complex scenes. CurveNet [4]} proposes that continuous descriptors (continuous sequences of point segments) are sufficient to estimate point cloud geometry.
Rotation Transformation Network (RTN) [5]} attempts to extract pose information from 3D objects for analyzing point cloud under self-supervision.
